Improving Mobile Robot Step-Climbing Capabilities With Center-of-Gravity Control
Bibliographic record
Abstract
The Mars Exploration Rover Spirit will henceforth be a stationary science platform, as its current mobility capabilities have been unable to free it from a sand trap for several months. Mobility systems of future planetary exploration robots will need to reduce risk of entrapment, access more challenging terrain, and cover more ground than ever before. This work presents a novel center-of-gravity (CoG) control algorithm, designed to increase rover mobility over step obstacles. The control algorithm is applied to a mobile robot platform featuring a reconfigurable chassis / suspension system. The 6-wheeled platform is equipped with active wheel-walking degrees of freedom in addition to driving, steering, and passive suspension DoFs typical for such robots. The controller is based on a new concept introduced here, the Contact-Angle Adjusted Support Plane, and uses wheel-ground contact angles to place the CoG such that weight acting on wheels encountering difficult local terrain is reduced. Mobility analysis, using the detailed multibody dynamics simulator RCAST (Rover Chassis Analysis and Simulation Tool), shows that the control algorithm dramatically improves step-climbing ability. The CoG controller can increase the height of obstacle surmountable by a factor of 2 to 3. Wheel-step interaction modes, joint angle limits for wheel-walking DoFs, and other practical mechanical considerations are discussed in the context of a hardware prototype of the studied chassis design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".